Academic Research Skills: Automate Literature Reviews with AI

TL;DR #

Academic Research Skills transforms Claude Code into a research assistant that can search papers, extract key findings, synthesize literature, and generate comprehensive reviews. With 31,628 stars, it automates the most time-consuming parts of academic research.

TL;DR: 31,628 stars — the leading AI-powered research automation framework.

What Are Academic Research Skills? #

Academic Research Skills is a modular skill system designed specifically for Claude Code that automates the end-to-end research pipeline. Instead of manually searching PubMed, arXiv, and Google Scholar, then reading each paper, then synthesizing findings into a coherent review, this framework chains specialized skills that handle each step.

The skill suite includes:

• Paper Search — Query academic databases (PubMed, arXiv, Semantic Scholar) with intelligent filtering
• PDF Extraction — Parse PDF papers, extract figures, tables, and key passages using a combination of PDF parsing and OCR for scanned documents
• Citation Analysis — Track citation networks, identify influential papers
• Synthesis Engine — Combine findings from multiple papers into structured summaries
• Literature Review Writer — Generate publication-ready literature reviews with proper citations

# Install Academic Research Skills
npx skills add https://github.com/Imbad0202/academic-research-skills

# List available research skills
npx skills list | grep research

How the Research Pipeline Works #

The research pipeline operates as a directed acyclic graph (DAG), where each skill’s output feeds into the next:

Query → Search → Filter → Extract → Analyze → Synthesize → Write

1. Query Formulation — You provide a research question or topic
2. Database Search — The search skill queries multiple academic databases simultaneously
3. Relevance Filtering — Papers are ranked by relevance using citation count, recency, and semantic similarity
4. PDF Extraction — Selected papers are downloaded and parsed for text, figures, and tables
5. Key Finding Extraction — NLP models extract claims, methods, results, and limitations
6. Cross-Paper Synthesis — Findings from all papers are compared and synthesized
7. Review Generation — A structured literature review is written with proper citations

# Example: Research pipeline for "transformer efficiency"
# Step 1: Search
python3 scripts/search.py --query "transformer model efficiency optimization" --databases arxiv,pubmed --max-results 50

# Step 2: Filter by relevance
python3 scripts/filter.py --input search_results.json --min-citations 10 --max-age 365

# Step 3: Extract key findings
python3 scripts/extract.py --papers filtered_papers.json --fields methods,results,limitations

# Step 4: Synthesize
python3 scripts/synthesize.py --extractions extractions.json --output synthesis.md

Installation & Setup #

Setting up Academic Research Skills requires Python 3.10+ and API access to academic databases:

# Clone the repository
curl -sL "https://github.com/Imbad0202/academic-research-skills/archive/refs/heads/main.zip" -o /tmp/research-skills.zip
unzip -q /tmp/research-skills.zip -d /tmp
cd /tmp/academic-research-skills-main

# Install dependencies
pip install -r requirements.txt

# Configure API keys
cp config.example.yaml config.yaml
# Edit config.yaml with your API keys

Required API Keys #

| Service | Purpose | Free Tier | |